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Table 1 Overview of the characteristics of all the datasets

From: How to predict the electronic health literacy of Chinese primary and secondary school students?: establishment of a model and web nomograms

Factor Testing = 0
(N = 363)
Training = 1
(N = 872)
EHL = 0
(N = 820)
EHL = 1
(N = 415)
p-value
Sex      0.81
 Male 188 (51.8%) 423 (48.5%) 408 (49.8%) 203 (48.9%)  
 Female 175 (48.2%) 449 (51.5%) 412 (50.2%) 212 (51.1%)  
Age 14.0 (11.0, 17.0) 14.0 (11.0, 17.0) 14.0 (11.0, 17.0) 16.0 (13.0, 17.0)  < 0.01
Race      0.41
 Han 360 (99.2%) 861 (98.7%) 809 (98.7%) 412 (99.3%)  
 Nhan 3 (0.8%) 11 (1.3%) 11 (1.3%) 3 (0.7%)  
Grade       < 0.01
 2 105 (28.9%) 243 (27.9%) 259 (31.6%) 89 (21.4%)  
 3 102 (28.1%) 246 (28.2%) 234 (28.5%) 114 (27.5%)  
 4 152 (41.9%) 375 (43.0%) 318 (38.8%) 209 (50.4%)  
 5 0 (0.0%) 1 (0.1%) 1 (0.1%) 0 (0.0%)  
 7 2 (0.6%) 7 (0.8%) 7 (0.9%) 2 (0.5%)  
 8 2 (0.6%) 0 (0.0%) 1 (0.1%) 1 (0.2%)  
Family size 4.0 (4.0, 4.0) 4.0 (4.0, 4.0) 4.0 (4.0, 4.0) 4.0 (4.0, 4.0) 0.81
Only child      0.84
 Yes 29 (8.0%) 99 (11.4%) 84 (10.2%) 44 (10.6%)  
 No 334 (92.0%) 773 (88.6%) 736 (89.8%) 371 (89.4%)  
Employment status       < 0.01
 Father working 97 (26.7%) 243 (27.9%) 244 (29.8%) 96 (23.1%)  
 Mother working 6 (1.7%) 27 (3.1%) 21 (2.6%) 12 (2.9%)  
 Parents both working 42 (11.6%) 109 (12.5%) 113 (13.8%) 38 (9.2%)  
 No parent working 218 (60.1%) 493 (56.5%) 442 (53.9%) 269 (64.8%)  
Location of the household       < 0.01
 City 138 (38.0%) 312 (35.8%) 324 (39.5%) 126 (30.4%)  
 Rural 225 (62.0%) 560 (64.2%) 496 (60.5%) 289 (69.6%)  
Mother education 2.0 (1.0, 3.0) 2.0 (1.0, 3.0) 2.0 (1.0, 3.0) 2.0 (1.0, 3.0) 0.07
Father education 2.0 (2.0, 3.0) 2.0 (2.0, 3.0) 2.0 (2.0, 3.0) 2.0 (2.0, 4.0) 0.55
Game time of online 2.0 (1.0, 3.0) 2.0 (0.0, 3.0) 2.0 (0.0, 3.0) 2.0 (1.0, 3.0) 0.91
Parental phubbing behavior 22.0 (17.0, 27.0) 22.0 (17.0, 27.0) 21.0 (17.0, 26.0) 24.0 (18.0, 28.0)  < 0.01
General self-efficacy 24.0 (20.0, 28.0) 24.0 (20.0, 28.0) 23.0 (20.0, 26.0) 27.0 (23.0, 31.0)  < 0.01